No Arabic abstract
We work with symmetric extensions based on L{e}vy Collapse and extend a few results of Arthur Apter. We prove a conjecture of Ioanna Dimitriou from her P.h.d. thesis. We also observe that if $V$ is a model of ZFC, then $DC_{<kappa}$ can be preserved in the symmetric extension of $V$ in terms of symmetric system $langle mathbb{P},mathcal{G},mathcal{F}rangle$, if $mathbb{P}$ is $kappa$-distributive and $mathcal{F}$ is $kappa$-complete. Further we observe that if $V$ is a model of ZF + $DC_{kappa}$, then $DC_{<kappa}$ can be preserved in the symmetric extension of $V$ in terms of symmetric system $langle mathbb{P},mathcal{G},mathcal{F}rangle$, if $mathbb{P}$ is $kappa$-strategically closed and $mathcal{F}$ is $kappa$-complete.
We obtain the lower bounds for ergodic convergence rates, including spectral gaps and convergence rates in strong ergodicity for time-changed symmetric L{e}vy processes by using harmonic function and reversible measure. As direct applications, explicit sufficient conditions for exponential and strong ergodicity are given. Some examples are also presented.
In a step reinforced random walk, at each integer time and with a fixed probability p $in$ (0, 1), the walker repeats one of his previous steps chosen uniformly at random, and with complementary probability 1 -- p, the walker makes an independent new step with a given distribution. Examples in the literature include the so-called elephant random walk and the shark random swim. We consider here a continuous time analog, when the random walk is replaced by a L{e}vy process. For sub-critical (or admissible) memory parameters p < p c , where p c is related to the Blumenthal-Getoor index of the L{e}vy process, we construct a noise reinforced L{e}vy process. Our main result shows that the step-reinforced random walks corresponding to discrete time skeletons of the L{e}vy process, converge weakly to the noise reinforced L{e}vy process as the time-mesh goes to 0.
L{e}vy walk is a popular and more `physical model to describe the phenomena of superdiffusion, because of its finite velocity. The movements of particles are under the influences of external potentials almost at anytime and anywhere. In this paper, we establish a Langevin system coupled with a subordinator to describe the L{e}vy walk in the time-dependent periodic force field. The effects of external force are detected and carefully analyzed, including nonzero first moment (even though the force is periodic), adding an additional dispersion on the particle position, the consistent influence on the ensemble- and time-averaged mean-squared displacement, etc. Besides, the generalized Klein-Kramers equation is obtained, not only for the time-dependent force but also for space-dependent one.
Motivated by the emph{L{e}vy foraging hypothesis} -- the premise that various animal species have adapted to follow emph{L{e}vy walks} to optimize their search efficiency -- we study the parallel hitting time of L{e}vy walks on the infinite two-dimensional grid.We consider $k$ independent discrete-time L{e}vy walks, with the same exponent $alpha in(1,infty)$, that start from the same node, and analyze the number of steps until the first walk visits a given target at distance $ell$.We show that for any choice of $k$ and $ell$ from a large range, there is a unique optimal exponent $alpha_{k,ell} in (2,3)$, for which the hitting time is $tilde O(ell^2/k)$ w.h.p., while modifying the exponent by an $epsilon$ term increases the hitting time by a polynomial factor, or the walks fail to hit the target almost surely.Based on that, we propose a surprisingly simple and effective parallel search strategy, for the setting where $k$ and $ell$ are unknown:The exponent of each L{e}vy walk is just chosen independently and uniformly at random from the interval $(2,3)$.This strategy achieves optimal search time (modulo polylogarithmic factors) among all possible algorithms (even centralized ones that know $k$).Our results should be contrasted with a line of previous work showing that the exponent $alpha = 2$ is optimal for various search problems.In our setting of $k$ parallel walks, we show that the optimal exponent depends on $k$ and $ell$, and that randomizing the choice of the exponents works simultaneously for all $k$ and $ell$.
Recent experiments have shown that photoluminescence decay of silicon nanocrystals can be described by the stretched exponential function. We show here that the associated decay probability rate is the one-sided Levy stable distribution which describes well the experimental data. The relevance of these conclusions to the underlying stochastic processes is discussed in terms of Levy processes.